The need for efficient and effective systems to classify and cluster data arises in many fields, including data management, science, finance, engineering, environmental monitoring, water supply systems, climate studies, health care, and many other areas of human activity. For example, many fields data involve collecting and analyzing large scale, complex datasets at high velocity (i.e., “big data.”). Big data may involve datasets of such vast scale that spotting trends or outcomes requires advanced application of analytic data science or knowledge processing (e.g., artificial intelligence). Classification and clustering needs arise for all types of data (e.g., text data, numeric data, image data, video data, etc.).
Conventional methods may include training machine learning algorithms, including neural network models, to predict or classify data. Conventional approaches typically include training and implementing an individual machine learning model. However, an individual model may reach an inaccurate result because the model may not be well-suited to the information it is attempting to classify, or it may lack appropriate training data (e.g., it may classify inaccurately a photo of a cat as a “rat.”). Further, an individual model may reach a sub-optimal result by failing to recognize distinguishing features of a data sample that indicate the data sample optimally belongs in an additional category or a sub-category (e.g., by sub-optimally classifying an image of a hairless cat as a “cat”).
Some conventional approaches may include training and implementing a plurality of models to classify and/or cluster data. For example, a data system may train and implement different models individually to classify and/or cluster data. However, training models individually may inefficiently waste resources. Such an approach may fail to take advantage of comparative strengths of various models. For example, one model may perform better when classifying faces, while another performs better when classifying animals, but when classifying images that includes humans and animals, a conventional approach may simply train the two models individually to cluster data without allowing the models to learn from each other.
For example, as shown in FIG. 2, a conventional approach to classifying a data sample may include using classification models that comprise machine learning models or other classification models. One or more classification models (e.g., classification models 204a, 204b, 204c, 204d, and 204n), may be configured to receive input data (e.g., an input data sample 202 such as an image of a hairless cat) and return corresponding classification results (classification results 206a, 206b, 206c, 206d, and 206n). A classification result may include a label (e.g., cat, dog, rat, etc.). Classification models may be configured to perform object recognition and classification to detect and classify, for example, animals in images. Classification models may use similar or may use different classification algorithms from each other.
FIG. 2 illustrates problems and challenges associated with conventional approaches to data classification and data clustering. For example, classification models of method 200 may produce different classification results for the same data sample, resulting in misclassification (i.e., inaccurate classification) or sub-optimal classification. A system may be unable to determine which classification results are accurate and may waste resources by training classification models with sub-optimal training conditions.
As shown, some classification models of FIG. 2 inaccurately classify the input image as depicting a rat or a naked mole rat. In some cases, classification models may sub-optimally classify the input image as cat but not the more optimal classification of a hairless cat. In the conventional approach depicted in FIG. 2, it may be inefficient or infeasible to train the plurality of models to generate an accurate and optimal classification. Further, traditional metrics of classification model output, such as a confidence interval, may provide incomplete information or may not correlate with a true classification. That is, traditional metrics may not indicate how well a classification model performs as compared to another classification model. In the example of FIG. 2, each classification model may report a high confidence level, for example, but only classification models 206a and 206d produce accurate results, and classification model 206a performs sub-optimally as compared to classification model 206d. Thus, the high confidence level may not reflect a true classification and it may be difficult to compare results.
Inaccurate and/or suboptimal classifications may arise in conventional approaches to classification. In conventional approaches, a classification model may be trained individually to meet performance criteria when learning to classify data (e.g., trained to minimize a loss function). Classification errors may arise, for example, when an individual classification model converges on a suboptimal number of classification categories during model training. During training, an classification model may reach a local minimum but fail to reach a global minimum of an optimization function. Some classification model may perform better than other classification models on some data samples. Hence, there is a need for unconventional approaches that improve the accuracy and efficiency of individual classification model classification results by learning from and incorporating the results of a plurality of classification models.
Therefore, conventional approaches suffer from inaccuracies and wasteful inefficient use of computing resources. In view of the shortcomings and problems with conventional approaches to clustering data, there is a need for unconventional approaches that improve the accuracy and efficiency of classification and clustering results by learning from and incorporating the results of a plurality of models.